Self-training methods. Some very recent solutions have sought to take advantage of recent
large pretrained encoder-decoder models via self-training [1]}, [2]}, [3]}, [4]}, [5]}.
The approach here is to create large number of pairs of
source review sets, paired with a pseudo or silver summary as an
approximate target. In all these methods,
one of the reviews from the source
set is taken as the pseudo summary, and other reviews or topically
related reviews to the target is taken as the set of source reviews.
This dataset is then used for further pretraining of encoder-decoder
transformer models to incorporate signals and language
specific to review summarization. These models are usually better
than unsupervised models based on generative approaches.
